Hydrotreatment of Olefins in Thermally Processed Bitumen under Mild Conditions
Bibliographic record
Abstract
The stabilization of olefins in a thermally processed bitumen is a focal area for the development of bitumen partial upgrading technologies. Although a number of approaches have been proposed, including alkylation, oligomerization, and adsorption, hydrotreatment is still the most effective strategy for treating olefins in thermally cracked products such as coker naphtha. In our previous work, we studied the hydrogenation of model olefin compounds to understand their reactivity under mild conditions. This paper is a follow-up study focusing on the hydrotreatment of olefins in a thermally processed bitumen using a bench-scale continuous hydroprocessing unit. Two different scenarios were investigated: (1) hydrotreating the olefin-rich light fraction (IBP-280 °C) of the thermally processed bitumen product and (2) hydrotreating the whole product. The cracked feedstock was prepared by processing oil sand bitumen under visbreaking conditions. It was found that on-specification product for olefin content (<1.0 wt % 1-decene equivalent) could be obtained by hydrotreating the light fraction at lower temperatures (∼275–300 °C) and with less hydrogen as compared to hydrotreating the whole bitumen product, for which temperatures close to 325 °C are required in addition to about double the hydrogen input. Hydrotreating the whole product, however, brings the benefit of markedly reducing the total acid number and increasing the American Petroleum Institute gravity, which can be helpful to achieve the product quality goals of partial upgrading. Product characterization by advanced techniques such as 1H NMR and two-dimensional gas chromatography has revealed interesting reactivity patterns of olefins, sulfur compounds, and aromatics during mild hydrotreatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".